Data visualization (sometimes referred to as scientific visualization, or just visualization) is a term applied to a variety of techniques and processes for the representation of, or transformation of, data or information into images--including graphs, pictures, or other graphical forms. For convenience of reference, the term "visualization" will be used herein to mean any non-mental technique, method or process for representing or transforming data into images. "Data," means information in any form, but especially digital or numerical information.
Often, a motivation for applying visualization techniques is a need or desire to abstract, distill, concentrate or otherwise transform a large amount of data to manageable, but meaningful, proportions. Some common typical applications include graphical representation of medical imaging data (i.e., for applications such as magnetic resonance imaging, or MRI), or weather satellite data.
An overall view of the application of visualization is presented in The Visual Display of Quantitative Information (1983) and Envisioning Information (1990), both authored by Edward Tufte and published by the Graphics Press, Cheshire, Conn. U.S.A.
In a paper by D. F. Andrews entitled "Plots of High-Dimensional Data," Biometrics, 28, 125-36, March 1972, the author applies a visualization approach based on a Fourier transformation to the analysis of static multi-dimensional biological data.
Various approaches to the application of visualization to computer program organization and operation are discussed in the paper by Gruia-Catalin and Cox, entitled "A Taxonomy of Program Visualization Systems," appearing in IEEE Computer Magazine, vol. 26, no. 12, December, 1993. There, the authors propose an animation-based mapping from the realm of the programmer to that of the viewer of a graphical representation.
Visualization has also been applied in a number of business and industrial contexts involving a number of system variables to be viewed simultaneously. These applications tend to use tables, bar graph and other icons, as well as composite curve plots.
In semiconductor device manufacture, data provided by monitoring equipment associated with an etching chamber are often used to effect selective exposure of semiconductor materials to controlled amounts of plasma etching. Etching environments of this type are used, e.g., in etching silicon wafers with a number of film layers. Etching generally proceeds through one film until another film is reached. It is usually desired to end the etch just after the breakthrough from one film to the other. Thus timing of the endpoint for the etching process is of considerable importance in the manufacture of semiconductor devices.
Data generated by monitoring equipment associated with the etch are usually processed by monitoring circuitry, either alone or in combination with a computer, to control the rate and/or timing of exposure to plasma in the chamber. This monitoring circuitry typically includes sensors, filters and logic for adapting the monitoring data to the intended control function. Thus, by monitoring variables such as pressure, gas flow, impedance and optical spectra associated with the plasma etching process, preferred timing for process endpoint is derived.
In industrial processes like the above-mention plasma etching process, product quality and yield can be adversely affected by erroneous etch intensity and duration. In many cases, however, the interaction between monitored variables or the complexity of monitoring data makes the prediction of endpoint timing difficult, inexact and not readily reproducible.